The project consists of the design and testing of a new system for computer aided medical diagnosis. The model will use a causal network representation of medical knowledge. The system will apply Bayes Theorem in an approximate way, to compute probabilities of disease, given patient attributes, in a way that will: a. account for the existance of multiple diagnoses in one patient. b. recognize conditional dependencies among attributes. The model will be developed and tested in the context of a specific problem area: the differential diagnosis of chest pain. The study will make use of a large data base that has already been collected on the clinical problem of chest pain. The medical knowledge in the model will be derived from two expert panels of physicians who will have access to the statistical data from one part of the data base (the "model building cases"). Then the performance of the model will be tested on the other part of the data base (the "model validation cases") and its performance will be compared with the performance of three other models: a standard Bayesian model, a linear discriminant model, and logistic regression model.